Sahab Tariq
Development
Punjab, Pakistan
Skills
Machine Learning (ML)
About
Sahab Tariq's skills align with IT R&D Professionals (Information and Communication Technology). Sahab also has skills associated with Programmers (Information and Communication Technology). Sahab Tariq has 8 years of work experience.
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Work Experience
Vision Intelligence and Machine Learning
August 2023 - August 2023
Machine Learning Modeling Pipelines in Production
December 2022 - May 2023
- Prompt Engineering and Advanced ChatGPT edX
PyCharm
November 2022 - November 2022
- Coursera
Sr. Machine Learning Engineer
AppsGenii Technologies
July 2021 - Present
- As a Senior Machine Learning Engineer, some of my roles and responsibilities are as follows; Resume Parsing and Question & Answering Techniques on unstructured • • data (Spacy, NLTK, Stanford NLP). Analyzed facial expression of a candidate for emotions, voice and stress • • level during the interview. Designing and developing machine learning and deep learning systems. • • Running machine learning tests and experiments. • Implementing appropriate ML algorithms. • Study and transform data science prototypes. • Design machine learning systems. • Research and implement appropriate ML algorithms and tools. • Develop machine learning applications according to requirements. • Select appropriate datasets and data representation methods. • Run machine learning tests and experiments. • Perform statistical analysis and fine-tuning using test results. • Train and retrain systems when necessary. • Extend existing ML libraries and frameworks. • Keep abreast of developments in the field. Skills: Linux, Python, NLTK, NLP, LSTM, Multiprocessing, OpenCV, Tensorflow, TensorRT, Keras, Socket.io, Shell & AWK, Huge CSV files, PyCharm, Sk-learn, PyTorch, Model stacking and etc.
Machine Learning Engineer
SlashNext
July 2018 - June 2021
- Some of my responsibilities are as follows; Natural language processing models in Python to predict phishing content • • using TF-IDF features and Random Forest, Three models are in production. Combined computer vision features with TF-IDF. • • Feature Engineering, Training, and Testing of Models, Finding New • Techniques, reporting Precision & Recall of models. Train Doc2vec on the phishing content to get the words Embedding. • Skills: Python, Linux, Shell & AWK, Huge CSV files, PyCharm, Sk-learn, PyTorch, Model stacking.
Computer Vision Developer
SIX Logics
June 2016 - June 2018
- As a Computer Vision Developer, my roles and responsibilities are as follows; Working on Object detection and tracking algorithms. • • Mapping of world coordinates to the 2D plane using homography. • Training of InceptionV2 model, and Keras image classifier. • Making of real-time system for analytics. Performance improved from 5 • fps to 33 fps. Direct communication with management, collaborating with other teams • • for projects. Skills: Linux, Python, Multiprocessing, OpenCV, Tensorflow, TensorRT, Keras, Socket.io and etc. Projects Project Name: Cell Segmentation Tech Stacks: MaskRCNN, OpenCV, Numpy, VIA tool for annotations, Deep Learning, Computer Vision. Description: The Cell Segmentation project is a Python-based application that utilizes advanced image processing techniques to accurately segment individual cells from microscopic images. Additionally, it calculates the center coordinates and area of each segmented cell, providing valuable data for cellular analysis and research. Project Name: Virtual Try-on Room Tech Stacks: Python, GANs, CNNs, PyTorch, Flask. Description: The Virtual Try Room is a Python-based project utilizing machine learning to virtually fit clothing onto user uploaded images. This technology offers an interactive, lifelike preview of clothing items on the user's image, enhancing their ability to make informed online purchase. Project Name: Shelf Detection Tech Stacks: Python, Gans, Cnns, Yolov5s, TensorFlow, Pytorch, Flask, microservices, celery, Redis, Docker, Rabbitmq, FastAPI. Description: The Shelf Detection project is a Python-based application designed to automate the process of identifying and cataloging items on a shelf through uploaded images. Leveraging sophisticated image processing techniques, including image stitching, this project provides a comprehensive view of the shelf's contents. Project Name: iVision Tennis Tech Stacks: Python, Flask, Microservices, object detection, object tracking, homography, sockets, javascript. Description: iVision Tennis is an innovative Python-based project that leverages deep learning techniques to transform real-time tennis streams into highly realistic, simulated content. By employing advanced image synthesis algorithms, the project creates a convincing virtual representation of the game, enhancing the viewing experience for tennis enthusiasts. Project Name: Emotion Detector Tech Stacks: OpenCV, HaarCascade, Deep Learning. Description:The Emotion Detector project is a Python-based application that employs transfer learning to accurately identify and classify emotions from images or video frames. By leveraging a pre-trained deep learning model, this project enables robust emotion recognition in real-time. Skills Artificial intelligence Data Analysis & Visualization Beautifulsoup HTML Selenium Data Scraping Scikit-learn Natural Language Processing Machine Learning C/C++ TensorFlow Keras Shell & AWK PyTorch NLTK Deep Learning Courses Data Gathering Scrapy Python XML Rotating Proxies Javascript Django Sockets OpenCV TensorRT Pandas MLOps
Education
